熊猫根据另一列的非空值创建一个新列

Question

I have a dataframe where I want to create a new column based on an existing column where the values are non null.

The existing column is in decimals and some rows are null. I want to create a new column in integers.

I am using lambda but keep getting a syntax error. Could anyone tell me what's wrong? Thanks

df['new'] =  df['old'].apply(lambda x: int(x) if x>=0)

I also tried:

df['new'] =  df['old'].apply(lambda x: int(x) if x.isnull == False)

and this one:

df['new'] =  df['old'].apply(lambda x: x.astype(int) if x>=0)

The syntax error is pointing to the last close parenthesis.

Answer 1

df['new'] =  df['old'].apply(lambda x: int(x) if x>=0)

您需要在三元运算符的末尾添加一个else 。

df['new'] =  df['old'].apply(lambda x: int(x) if x>=0 else 'Nope')

Answer 2

You got syntax error, because your lambda function is not correct. Specifically, the if ... else ... conditional expression is wrong. The conditional expression must be

conditional_expression ::=  or_test [“if” or_test “else” expression]

You were missing else part.

Another thing I would like to mention is that the graceful way to convert the data type is to use astype function . If you want to cast data on some condition, you could do like:

new = df.loc[df.old>0].astype('int')

Then new would become a Series you need.

Thanks.